[scikit-learn] using a predictor as transformer

[Andreas Mueller]

If you want to use lasso for feature selection in a pipeline you have to 
wrap it in SelectFromModel.


On 09/06/2017 11:56 AM, Lefevre, Augustin wrote:
>
> Hi all,
>
> I am playing with the pipeline features of sklearn and it seems that I 
> can’t use a prediction algorithm as intermediate step.
>
> For instance, in the example below I use the output of a lasso as an 
> additional feature to feed a random forest, in such a way that feature 
> selection the Lasso does some preliminary feature selection.
>
> But I get a : “TypeError: All estimators should implement fit and 
> transform.”
>
> So I would like to add a transform method to the Lasso estimator so 
> that it can be used in a FeatureUnion. Is that possible ?
>
> Best regards
>
> X=np.hstack((np.random.randn(500,10),np.random.randint(0,10,(500,10)))) 
> /# regressor variables
> /y=np.random.randn(500) /# target variable
> /ct_get = FunctionTransformer(*lambda *d:d[0:10]) /# transformer to 
> extract continuous variables
> /dt_get = FunctionTransformer(*lambda *d:d[11:20]) /# transformer to 
> extract discrete variables
>
> # first step is a regression pipeline
> /reg = 
> Pipeline([(*'ct_vars'*,ct_get),(*'scaler'*,StandardScaler()),(*'poly'*,PolynomialFeatures(degree=3)),(*'lasso'*,Lasso())])
> /# A random forest feeds on the discrete part of the data + one 
> continuous variable
> /estimator = 
> Pipeline([(*'level1'*,FeatureUnion([(*'dt_vars'*,dt_get),(*'reg'*,reg)])),(*'rf'*,RandomForestRegressor())])
>
> estimator.fit(X,y)
> *print **"R^2 score is :"*,estimator.score(X,y)
>
> *Augustin LEFEVRE*|**Consultant Senior |Ykems| -
>
>  M : +33 7 77 97 94 89 | alefevre at ykems.com 
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